These functions compute the point estimate and confidence interval for
Cramer's V.

```
cramersV(x, y = NULL, digits = 2)
# S3 method for CramersV
print(x, digits = x$input$digits, ...)
confIntV(
x,
y = NULL,
conf.level = 0.95,
samples = 500,
digits = 2,
method = c("bootstrap", "fisher"),
storeBootstrappingData = FALSE
)
# S3 method for confIntV
print(x, digits = x$input$digits, ...)
```

## Arguments

- x
Either a crosstable to analyse, or one of two vectors to use to
generate that crosstable. The vector should be a factor, i.e. a categorical
variable identified as such by the 'factor' class).

- y
If x is a crosstable, y can (and should) be empty. If x is a
vector, y must also be a vector.

- digits
Minimum number of digits after the decimal point to show in
the result.

- ...
Any additional arguments are passed on to the `print`

function.

- conf.level
Level of confidence for the confidence interval.

- samples
Number of samples to generate when bootstrapping.

- method
Whether to use Fisher's Z or bootstrapping to compute the
confidence interval.

- storeBootstrappingData
Whether to store (or discard) the data
generating during the bootstrapping procedure.

## Value

A point estimate or a confidence interval for Cramer's V, an effect
size to describe the association between two categorical variables.

## Examples

```
### Get confidence interval for Cramer's V
### Note that by using 'table', and so removing the raw data, inhibits
### bootstrapping, which could otherwise take a while.
confIntV(table(infert$education, infert$induced));
#> Cramér's V 95% confidence interval (point estimate = .18):
#> Using Fisher's z: [.06; .3]
```